BLIP-2 ViT-G OPT 2.7B (fine-tuned)

Reported on 4 benchmarks across 2 tasks · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Natural Language Processing4 results

Visual Question Answering (VQA)onVQA v2 test-dev
Accuracy· 2023-01-30
81.74
best: 84.3 (PaLI)
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models arXiv:2301.12597
Visual Question Answering (VQA)onVQA v2 val
Accuracy· 2023-01-30
81.59
best: 82.19 (BLIP-2 ViT-G OPT 6.7B (fine-tuned))
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models arXiv:2301.12597
Visual Question AnsweringonVQA v2 test-dev
Accuracy· 2023-01-30
81.74
best: 82.3 (BLIP-2 ViT-G OPT 6.7B (fine-tuned))
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models arXiv:2301.12597
Visual Question AnsweringonVQA v2 val
Accuracy· 2023-01-30
81.59
best: 82.19 (BLIP-2 ViT-G OPT 6.7B (fine-tuned))
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models arXiv:2301.12597